Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Ranking Metric Embedding for Next New POI Recommendation
Authors: Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, Quan Yuan
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the stateof-the-art next POI recommendation methods. |
| Researcher Affiliation | Academia | 1Interdisciplinary Graduate School, Nanyang Technological University, Singapore, EMAIL 2School of Computer Engineering, Nanyang Technological University, Singapore, {lixutao@, gaocong@, qyuan1@e.}ntu.edu.sg 3School of Computing, Teesside University, UK, EMAIL 4School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, EMAIL |
| Pseudocode | Yes | Algorithm 1: PRME |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | Yes | We use two publicly available datasets. The first dataset is the Four Square check-ins within Singapore [Yuan et al., 2013] while the second one is the Gowalla check-ins dataset within California and Nevada [Cho et al., 2011]. |
| Dataset Splits | Yes | For the one-year check-ins data, we use the check-ins in the first 10 months as training set, the 11th month as tuning set, and the last month as test set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions software like 'Matlab' in related work (Table 2 in some versions, but not this PDF), but does not specify any software dependencies with version numbers for their own experimental setup. |
| Experiment Setup | Yes | In the experiments, we use the two datasets introduced in Section 3. For the one-year check-ins data, we use the check-ins in the first 10 months as training set, the 11th month as tuning set, and the last month as test set. We exploit two wellknown measure metrics [Yuan et al., 2013], namely Precision@N and Recall@N (denoted by Pre@N and Rec@N respectively). Given a user and his current location, we use the next check-in in successive τ hours as the ground truth. The time window threshold τ is set at 6 hours following [Cheng et al., 2013]. Based on the tuning set, the number of dimensions is set at K = 60, learning rate γ = 0.005, regularization term λ = 0.03 and component weight α = 0.2. |